The Sched app allows you to build your schedule but is not a substitute for your event registration. You must be registered for PyTorch Conference Europe 2026 to participate in the sessions. If you have not registered but would like to join us, please go to the event registration page to purchase a registration.
This schedule is automatically displayed in CEST (UTC/GMT +2). To see the schedule in your preferred timezone, please select from the drop-down menu to the right, above "Filter by Date."
Sign up or log in to add sessions to your schedule and sync them to your phone or calendar.
Asynchronous Reinforcement Learning (AsyncRL) workloads have unique data sharing requirements: actors must efficiently exchange large tensors across processes and nodes, often with different sharding configurations—not just at checkpoint time, but continuously during training for live weight synchronization. This talk presents Torchstore, an open-source distributed tensor storage system built on Monarch actors that tackles these challenges. We'll share the key lessons learned—from designing pluggable transport backends (RDMA, shared memory, RPC) to implementing transparent live DTensor resharding that lets producers and consumers use entirely different parallelism strategies. We'll also discuss the friction we encountered integrating with inference engines like vLLM, where differing model definitions and integrations present new bottlenecks. Whether you're building actor-based training systems or thinking about disaggregated training-inference architectures, you'll leave with practical insights on distributed tensor storage design.
Lucas has been developing Machine Learning Applications and Machine Learning infrastructure at scale for years, and has recently been focused on extending the product offering of PyTorch's Distributed Checkpointing stack.
